How do you deploy the machine learning model to production?
Deploy your first ML model to production with a simple tech stack
- Training a machine learning model on a local system.
- Wrapping the inference logic into a flask application.
- Using docker to containerize the flask application.
- Hosting the docker container on an AWS ec2 instance and consuming the web-service.
How do you monitor ML models in production?
The most straightforward way to monitor your ML model is to constantly evaluate your performance on real-world data. You could customize triggers to notify you when there are significant changes in metrics such as accuracy, precision, or F1.
How do you deploy keras models to production?
Deploy a Keras Deep Learning Project to Production with Flask
- Define your goal.
- Load data.
- Data exploration.
- Data preparation.
- Build and evalute your model.
- Save the model.
- Build REST API.
- Deploy to production.
Why you need to manage your ML models after deployment?
Model Monitoring is an operational stage in the machine learning life cycle that comes after model deployment, and it entails ‘monitoring’ your ML models for things like errors, crashes, and latency, but most importantly, to ensure that your model is maintaining a predetermined desired level of performance.
Can a ML model file be deployed on a production machine?
But, simply deploying your ML model file on the production machine may not be sufficient, as only a handful of consumers who have access to your production machine, will be able to use it. In most of the cases, the consumers of your model may not limited to your team members, who have access to your production machine.
How to deploy a machine learning model in production?
One way to deploy your ML model is, simply save the trained and tested ML model ( sgd_clf ), with a proper relevant name (e.g. mnist ), in some file location on the production machine. The consumers can read (restore) this ML model file ( mnist.pkl) from this file location and start using it to make predictions on their dataset.
Can a ML project be successful after deployment?
But either way ML projects can only be successful after a model has been deployed and its predictions are being served. Surprisingly machine learning deployment is rarely discussed online.
What do I need to train my ML model?
Your model isn’t going to train, run, and deploy itself. For that, you need frameworks and tooling, software and hardware that help you effectively deploy ML models.